摘要
提出一种基于神经网络的预测驾驶座椅主观舒适度的方法。预测的输入变量为12个体压分布变量以及2个人体变量,输出变量为整体舒适度指数。数据通过12个被试者对5辆不同汽车的驾驶座椅测试获得。在对数据预测时,对主观舒适度的预测实验结果达到了均方误差0.003 0以及相关系数0.749的效果。研究的成果将有助于降低当前汽车制造企业提高汽车座椅舒适性过程中的成本,并缩短制造时间。
An evaluation method based on neural network is put forward for the purpose of predicting subjective perceptions of automobile comfort seat. The inputs includs 12 seat interface pressure measures and two anthropometrics. The output is an overall comfort index derived from a survey. The SVR is developed and validated by using data collected from 12 subjects, and the subjects is evaluated by five different driving seats. The prediction results reach 0.0030 in mean square error and 0.749 in linear correlation coefficient, it gives better results than that with artificial neural network. The resulting knowledge can reduce the cost and time associated with the current automobile seat comfort development process.
出处
《湖南工业大学学报》
2007年第5期33-35,共3页
Journal of Hunan University of Technology
关键词
驾驶座椅
静态舒适度
体压分布
神经网络
driving seat
comfort
body pressure distribution
neural network
